Design Optimization Study Research Articles

Glucose Sensor Design Based on Monte Carlo Simulation.

Continuous glucose monitoring based on the minimally invasive implantation of glucose sensor is characterized by high accuracy and good stability. At present, glucose concentration monitoring based on fluorescent glucose capsule sensor is a new development trend. In this paper, we design a fluorescent glucose capsule sensor with a design optimization study. The motion trajectory of incident light in the fluorescent gel layer is simulated based on the Monte Carlo method, and the cloud maps of light intensity with the light intensity distribution at the light-receiving layer are plotted. Altering the density of fluorescent molecules, varying the thickness of tissue layers, and adjusting the angle of incidence deflection, the study investigates the influence of these parameter changes on the optimal position of reflected light at the bottom. Finally, the simulation results were utilized to design and fabricate a fluorescent glucose capsule sensor. Rabbit subcutaneous tissue glucose level tests and real-time glucose solution concentration monitoring experiments were performed. This work contributes to the real-time monitoring of glucose levels and opens up new avenues for research on fabricating glucose sensors.

Oral supplementation with propionate is reflected in the serum of healthy individuals.

Short-chain fatty acids (SCFAs), including propionic acid (PA), are key in immunological research. Supplementing PA has shown benefits for autoimmune diseases. A comprehensive understanding of the PA pharmacokinetics is essential for the optimal design and execution of studies utilizing orally administered PA. We propose two methods of measuring PA in serum, carried out by different laboratories. Blood samples from 20 volunteers were collected hourly following PA supplementation. Serum propionate quantification was performed with two independent mass spectrometry-based (MS) analyses, including liquid-chromatography (LC)-MS and direct-infusion (DI)-MS. PA levels increased within 1 h of ingestion of 500 mg PA. Serum concentrations ranged from 1.3 to 4.5 µmol/L, rising significantly after 1 h (p < 0.05). Serum levels returned to baseline within 2 h. No significant differences were found regarding sex or diet. The shown pharmacokinetics can be used in future PA research.

MeSS and assembly_finder: a toolkit for in silico metagenomic sample generation.

The intrinsic complexity of the microbiota combined with technical variability render shotgun metagenomics challenging to analyze for routine clinical or research applications. In silico data generation offers a controlled environment allowing for example to benchmark bioinformatics tools, to optimize study design, statistical power, or to validate targeted applications. Here, we propose assembly_finder and the Metagenomic Sequence Simulator (MeSS), two easy-to-use Bioconda packages, as part of a benchmarking toolkit to download genomes and simulate shotgun metagenomics samples, respectively. Outperforming existing tools in speed while requiring less memory, MeSS reproducibly generates accurate complex communities based on a list of taxonomic ranks and their abundance. All code is released under MIT License and is available on https://github.com/metagenlab/MeSS and https://github.com/metagenlab/assembly_finder.

Development of the Dutch translational knowledge agenda for inherited metabolic diseases.

Inherited metabolic diseases (IMDs) may have considerable implications for patients and their families. Despite their individual rarity, covering a spectrum of over 1800 distinct diseases, the diseases collectively exert a significant impact, with often lifelong disabilities. The United for Metabolic Diseases consortium was established to catalyze research with translation into the best possible care. To generate a translational knowledge agenda, which identifies and prioritizes research questions, directly relevant to patient care or for IMD patients and their families. Following a process established by the Knowledge Institute of the Dutch Association of Medical Specialists, we generated a comprehensive translational knowledge agenda for IMDs. A multidisciplinary steering committee, composed of 12 diverse metabolic experts collected research questions through an online questionnaire using snowballing. The 462 proposed questions were categorized and prioritized during a meeting attended by 22 representatives of all stakeholder groups. The resulting top 10 research questions cover multiple themes, i.e. prediction of disease progression, development of novel tools, mechanistic insights, improved diagnostics, therapeutic integration of multi-omics techniques, assessment of impact on daily life, expanding treatment avenues, optimal study designs, effect of lifestyle interventions, and data utilization using FAIR principles. This collective endeavor reflects the collaborative spirit needed for rare disease research. This knowledge agenda will guide funding directions and applications but will also boost interdisciplinary collaboration to push the field of IMDs research forward in a renewed UMD consortium. Patient engagement, transparency, and a comprehensive approach make this knowledge agenda a pivotal step toward addressing the pressing research needs and priorities in this domain.

Study design features increase replicability in brain-wide association studies

Brain-wide association studies (BWAS) are a fundamental tool in discovering brain–behaviour associations1,2. Several recent studies have shown that thousands of study participants are required for good replicability of BWAS1, 2–3. Here we performed analyses and meta-analyses of a robust effect size index using 63 longitudinal and cross-sectional MRI studies from the Lifespan Brain Chart Consortium4 (77,695 total scans) to demonstrate that optimizing study design is critical for increasing standardized effect sizes and replicability in BWAS. A meta-analysis of brain volume associations with age indicates that BWAS with larger variability of the covariate and longitudinal studies have larger reported standardized effect size. Analysing age effects on global and regional brain measures from the UK Biobank and the Alzheimer’s Disease Neuroimaging Initiative, we showed that modifying study design through sampling schemes improves standardized effect sizes and replicability. To ensure that our results are generalizable, we further evaluated the longitudinal sampling schemes on cognitive, psychopathology and demographic associations with structural and functional brain outcome measures in the Adolescent Brain and Cognitive Development dataset. We demonstrated that commonly used longitudinal models, which assume equal between-subject and within-subject changes can, counterintuitively, reduce standardized effect sizes and replicability. Explicitly modelling the between-subject and within-subject effects avoids conflating them and enables optimizing the standardized effect sizes for each separately. Together, these results provide guidance for study designs that improve the replicability of BWAS.

Analyzing aptamer structure and interactions: in silico modelling and instrumental methods.

Aptamers are short oligonucleotides that bind specifically to various ligands and are characterized by their low immunogenicity, thermostability, and ease of labeling. Many biomedical applications of aptamers as biosensors and drug delivery agents are currently being actively researched. Selective affinity selection with exponential ligand enrichment (SELEX) allows to discover aptamers for a specific target, but it only provides information about the sequence of aptamers; hence other approaches are used for determining aptamer structure, aptamer-ligand interactions and the mechanism of action. The first one is in silico modelling that allows to infer likely secondary and tertiary structures and model their interactions with a ligand. The second approach is to use instrumental methods to study structure and aptamer-ligand interaction. In silico modelling and instrumental methods are complimentary and their combined use allows to eliminate some ambiguity in their respective results. This review examines both the advantages and limitations of in silico modelling and instrumental approaches currently used to study aptamers, which will allow researchers to develop optimal study designs for analyzing aptamer structure and ligand interactions.

Impact of in-silico technologies in the development of high-risk medical devices on healthcare

Abstract This study examines the impact of in-silico technologies (IST), such as virtual cohorts and digital twins, on the development of high-risk medical devices within the healthcare sector. An iterative empirical approach combining a literature review, exploratory and in-depth interviews with stakeholders from academia, industry, regulators, healthcare professionals, and focus groups with patients across Europe, Japan, and the USA was employed to gain insight into the implications of IST on healthcare. The findings indicate that IST can reduce the time to market for medical devices, thereby facilitating faster, broader, and more equitable access for patients. The implementation of extensive early testing, optimized study designs and increased sample sizes in trials can facilitate the achievement of enhanced safety. The early identification of adverse effects can enhance the safety of medical devices and improve patient outcomes. Furthermore, IST may facilitate the inclusion of underrepresented groups, such as children, in clinical trials, thereby enhancing their representation in medical research. The reduction in costs associated with IST may facilitate innovation cycles. However, the impact on prices is contingent on competition. Furthermore, IST have the potential to reduce the need for animal testing. Nevertheless, several uncertainties and potential adverse effects have been identified. Physician training on new devices remains a bottleneck, which may limit the benefits of new products. Despite the advancements, a complete replacement of traditional clinical and preclinical trials with IST is not seen to be feasible. From a policy perspective, it is crucial to promote regulatory clarity by providing guidance for the usage of IST, to offer incentives for the adoption of IST, and to foster competition. These findings demonstrate the multifaceted potential benefits and challenges of IST, including enhanced safety, accessibility, and innovation in healthcare. Key messages • In-silico technologies can reduce time-to market of medical devices up medical device and improve safety for patients. • Challenges like physician training and regulatory guidance must be addressed to fully realize benefits of in silico technologies.

Effect of Design Parameters on Buckling Tendency of an Eccentric Drag Link Used in a Truck Steering Linkage: A DoE/RSM-Based Design Optimisation Study

The steering system, deemed one of the most critical subsystems for ensuring the safety of a vehicle, is characterised by its pivotal role in manoeuvrability and control. The primary function of transmitting the steering input from the steering wheel to the vehicle's wheels is carried out by the drag link within the steering system. Therefore, the durability and proper functioning of the drag link in vehicles, especially carrying heavy loads, are of critical importance for both safety and efficiency. The main purpose of this study is to determine the optimal geometry of the drag link utilised in the steering system of a 4x2 truck using the Design of Experiments-Response Surface Methodology (DoE-RSM). Firstly, an idealised worst-case load model was used to determine the maximum force applied by the Pitman arm on the drag link. The stress distribution and deformation on the drag link caused by the maximum design load were deter-mined using Finite Element Analysis (FEA). Based on the results of the analyses, eccentricity, rod thickness, and fillet radius were defined as design parameters. The parameter ranges were determined by considering the volume swept by the wheel and buckling criteria under maximum load conditions. According to the DoE-RSM results, concerning the local sensitivity per-centages of the parameters on total deformation, it was observed that the eccentricity (e) parameter has the most significant impact, with an approximate positive rate of 74%.

Enhanced Displacement Magnification in Symmetrical Differential Levers: A Compliant Mechanism Design Optimization Study

Enhanced Displacement Magnification in Symmetrical Differential Levers: A Compliant Mechanism Design Optimization Study

Expert opinion on design and endpoints for studies on catheter ablation of atrial fibrillation.

Catheter ablation of atrial fibrillation (AF) is frequently studied in randomized trials, observational and registry studies. The aim of this expert opinion is to provide guidance for clinicians and industry regarding the development of future clinical studies on catheter ablation of AF, implement lessons learned from previous studies, and promote a higher degree of consistency across studies. Studies on catheter ablation of AF may benefit from well-described definitions of endpoints and consistent methodology and documentation of outcomes related to efficacy, safety and cost-effectiveness. The availably of new, innovative technologies warrants further consideration about their application and impact on study design and the choice of endpoints. Moreover, recent insights gained from AF ablation studies suggest a reconsideration of some methodological aspects. A panel of clinical experts on catheter ablation of AF and designing and conducting clinical studies developed an expert opinion on the design and endpoints for studies on catheter ablation of AF. Discussions within the expert panel with the aim to reach consensus on predefined topics were based on outcomes reported in the literature and experiences from recent clinical trials. A comprehensive set of recommendations is presented. Key elements include the documentation of clinical AF, medication during the study, repeated ablations and their effect on endpoint assessments, postablation blanking and the choice of rhythm-related and other endpoints. This expert opinion provides guidance and promotes consistency regarding design of AF catheter ablation studies and identified aspects requiring further research to optimize study design and methodology. Recent insights from studies on catheter ablation of atrial fibrillation (AF) and the availability of new innovative technologies warrant reconsideration of methodological aspects related to study design and the choice and assessment of endpoints. This expert opinion, developed by clinical experts on catheter ablation of AF provides a comprehensive set of recommendations related to these methodological aspects. The aim of this expert opinion is to provide guidance for clinicians and industry regarding the development of clinical studies, implement lessons learned from previous studies, and promote a higher degree of consistency across studies.

A Proposed Framework for Rigor and Transparency in Dysphagia Research: Prologue.

Scientific transparency and rigor are essential for the successful translation of knowledge in clinical research. However, the field of oropharyngeal dysphagia research lacks guidelines for methodological design and reporting, hindering accurate interpretation and replication. This article introduces the Framework for RigOr aNd Transparency In REseaRch on Swallowing (FRONTIERS), a new critical appraisal tool intended to support optimal study design and results reporting. The purpose of introducing FRONTIERS at this early phase is to invite pilot use of the tool and open commentary. FRONTIERS was developed by collaborating researchers and trainees from six international dysphagia research labs. Eight domains were identified, related to study design, swallowing assessment methods, and oropharyngeal dysphagia intervention reporting. Small groups generated questions capturing rigor and transparency for each domain, based on examples from the literature. An iterative consensus process informed the refinement and organization of primary and subquestions, culminating in the current initial version of FRONTIERS. FRONTIERS is a novel tool, intended for use by oropharyngeal dysphagia researchers and research consumers across disciplines. A web application enables provisional use of the tool, and an accompanying survey solicits feedback regarding the framework. FRONTIERS seeks to foster rigor and transparency in the design and reporting of oropharyngeal dysphagia research. We encourage provisional use and invite user feedback. A future expert consensus review is planned to incorporate feedback. By promoting scientific rigor and transparency, we hope that FRONTIERS will support evidence-based practice and contribute to improved health outcomes for individuals with oropharyngeal dysphagia.

Crushing performance of an additively manufactured bio-inspired hybrid energy absorption profile

Abstract Bio-inspired structures have applications in various industries, including automotive, defense, aerospace, and biomedical industries, owing to their combination of high-strength and lightweight properties. To enhance their energy absorption performance, a novel design was developed by integrating a spiral component, inspired by the cross section of the date palm tree trunk (Latin: Phoenix dactylifera), into an empty aluminum tube. The energy absorption performance of a bio-inspired hybrid energy-absorbing profile (BIHEAP) was experimentally and numerically investigated. To ensure the reliability of the numerical studies, finite element models were generated using ANSYS LS-DYNA and subsequently validated through axial crushing tests. Design optimization studies were carried out using surrogate-based models, such as the response surface model and Kriging surrogate models, to increase the energy absorption performance of the BIHEAP, which has three different design variables (spiral revolution, wall thickness, and number of spiral tubes). The initial design of the BIHEAP exhibited a specific energy absorption capacity (SEA) and crush force efficiency (CFE) that surpassed those of the empty aluminum tube by 17.2 % and 4.6 %, respectively. The optimized BIHEAP design demonstrated SEA and CFE values that were 21.4 % and 32 % greater than those of the empty aluminum tube, respectively. When the initial and optimized BIHEAP design were compared, it was found that SEA and CFE was increased by 3.5 % and 26.1 %, respectively.

A case study of multi-objective design optimization of a healthy building in Shanghai, China

Energy efficient healthy buildings design is important in achieving carbon neutrality and occupants’ health, yet not sufficiently explored. This paper aims to optimize a healthy building in Shanghai, China, based on energy consumption, indoor air quality and visual comfort. A four-step optimization method was proposed. Firstly, Latin Hypercube-Soubert sampling method was used to generate 375 design samples and performance data through computer simulation and calculation. Secondly, five different machine learning approaches were applied for prediction model development. Thirdly, the best prediction models were coupled with eleven optimization algorithms to find Pareto solutions. Finally, optimization on different combinations of design parameters were conducted. It was found that the back propagation neural network and Pareto Envelope-based Selection Algorithm II were the best prediction models and optimization algorithm. The average reduction of building energy consumption, visual discomfort, and improper indoor air quality hours were found to be 25.73 %, 46.24 %, and 38.34 %, respectively. The recommended windows to wall ratios of the east, south, west and north walls, absorptance of solar radiation and filter types are in the range of 20%–35 %, 10%–45 %, 10%–30 %, 10%–40 %, 0.7–0.9, and S7-S9, respectively. This study contributes to enrich the case study of healthy buildings, provide a quick design optimization approach and analysis on the importance of each design parameter. Its originality lies in the optimization methodology, comparative analysis of prediction models, optimization algorithms, and combination effects of design parameters. The outcomes can guide the building designers in performing energy efficient design while maximizing indoor air quality and visual comfort.

Investigating Best Practices for Ecological Momentary Assessment: Nationwide Factorial Experiment.

Ecological momentary assessment (EMA) is a measurement methodology that involves the repeated collection of real-time data on participants' behavior and experience in their natural environment. While EMA allows researchers to gain valuable insights into dynamic behavioral processes, the need for frequent self-reporting can be burdensome and disruptive. Compliance with EMA protocols is important for accurate, unbiased sampling; yet, there is no "gold standard" for EMA study design to promote compliance. The purpose of this study was to use a factorial design to identify optimal study design factors, or combinations of factors, for achieving the highest completion rates for smartphone-based EMAs. Participants recruited from across the United States were randomized to 1 of 2 levels on each of 5 design factors in a 2×2×2×2×2 design (32 conditions): factor 1-number of questions per EMA survey (15 vs 25); factor 2-number of EMAs per day (2 vs 4); factor 3-EMA prompting schedule (random vs fixed times); factor 4-payment type (US $1 paid per EMA vs payment based on the percentage of EMAs completed); and factor 5-EMA response scale type (ie, slider-type response scale vs Likert-type response scale; this is the only within-person factor; each participant was randomized to complete slider- or Likert-type questions for the first 14 days or second 14 days of the study period). All participants were asked to complete prompted EMAs for 28 days. The effect of each factor on EMA completion was examined, as well as the effects of factor interactions on EMA completion. Finally, relations between demographic and socioenvironmental factors and EMA completion were examined. Participants (N=411) were aged 48.4 (SD 12.1) years; 75.7% (311/411) were female, 72.5% (298/411) were White, 18.0% (74/411) were Black or African American, 2.7% (11/411) were Asian, 1.5% (6/411) were American Indian or Alaska Native, 5.4% (22/411) belonged to more than one race, and 9.6% (38/396) were Hispanic/Latino. On average, participants completed 83.8% (28,948/34,552) of scheduled EMAs, and 96.6% (397/411) of participants completed the follow-up survey. Results indicated that there were no significant main effects of the design factors on compliance and no significant interactions. Analyses also indicated that older adults, those without a history of substance use problems, and those without current depression tended to complete more EMAs than their counterparts. No other demographic or socioenvironmental factors were related to EMA completion rates. Finally, the app was well liked (ie, system usability scale score=82.7), and there was a statistically significant positive association between liking the app and EMA compliance. Study results have broad implications for developing best practices guidelines for future studies that use EMA methodologies. ClinicalTrials.gov number NCT05194228; https://clinicaltrials.gov/study/NCT05194228.

Reliability-based Design Optimization (RBDO) Study Applied to the Thermal Management of a Multi-Led Chip Package

Solid-state lighting based on LEDs is used in various applications, including display, communications, etc. However, the high junction temperature is still challenging due to the LED chip’s reduced light output and lifetime. To face this challenge, a thermal study was done to determine junction temperature TJ of a multi-led chip package. Then, a sensitivity analysis of different materials was performed using the Sobol method to identify the parameters that most influence the junction temperature. To calculate the probability of failure of the model, the FORM-SORM and Monte Carlo methodologies were employed in this study. It was obtained that the probability of failure of the LED package is roughly 20%. An optimum design was created using reliability-based design optimization (RBDO) to lower this percentage. After application of this method, the junction temperature was lowered by 11% and the reliability level increased from 80% to 91%.

Research on the modeling technique of infrared radiation scaling law for rocket engine exhaust plumes

The complexity of the rocket motor exhaust plume radiation phenomenon, along with the influence of numerous parameters and implicit propagation, makes the theoretical simulation calculations expensive. This hinders design optimization, parameter identification, and other studies. Hence, it is crucial to establish the direct correlation between thrust, velocity, altitude, detection angle, and plume radiation in engineering.The paper proposes a scaling law modeling approach for predicting plume infrared radiation from liquid oxygen (LOX)/kerosene rocket engines to solve the difficulties of quick prediction and lack of explicit models. An explicit scaling law model with four parameters is developed by utilizing the e-exponential scaling law to relate plume radiance to flight velocity, the power scaling law to relate plume radiance to vacuum thrust, and the sinusoidal scaling law to relate plume radiance to detection angle. This model employs a decoupled modeling approach for plume radiance, flight altitude, flight velocity, vacuum thrust, and detection angle. A fast prediction method for the LOX/kerosene rocket engine plume in the altitude range of 11∼61 km is achieved. The theoretical simulation test shows the relative error of the velocity-scaling law in predicting the radiance of the RD-180 template engine during the Atlas III flight trajectory is less than 20 %. The radiance of the RD-170 and RD-191 rocket engines is predicted using the thrust-scaling law based on the radiance of the RD-180 template engine exhaust plume. The radiance of the exhaust plume from the RD-170 and RD-191 rocket motors is estimated using the thrust-scaling law with an accuracy of under 15 %. Subsequently, the radiance of these two motors at detection angles between 10° and 170° is predicted using the angle-scaling with a relative error of less than 35 %.

Immobilization of Bacillus subtilis bacteria into Biohybrid Crosslinked Chitosan-glutaraldehyde for acid red 88 Dye Removal: Box–Behnken Design Optimization and Mechanism Study

Immobilization of Bacillus subtilis bacteria into Biohybrid Crosslinked Chitosan-glutaraldehyde for acid red 88 Dye Removal: Box–Behnken Design Optimization and Mechanism Study

Reduced-order model to approximate response matrices for filter stack spectrometers.

We present a reduced-order model to calculate response matrices rapidly for filter stack spectrometers (FSSs). The reduced-order model allows response matrices to be built modularly from a set of pre-computed photon and electron transport and scattering calculations through various filter and detector materials. While these modular response matrices are not appropriate for high-fidelity analysis of experimental data, they encode sufficient physics to be used as a forward model in design optimization studies of FSSs, particularly for machine learning approaches that require sampling and testing a large number of FSS designs.

Accurate EVSI Estimation for Nonlinear Models Using the Gaussian Approximation Method.

The expected value of sample information (EVSI) measures the expected benefits that could be obtained by collecting additional data. Estimating EVSI using the traditional nested Monte Carlo method is computationally expensive, but the recently developed Gaussian approximation (GA) approach can efficiently estimate EVSI across different sample sizes. However, the conventional GA may result in biased EVSI estimates if the decision models are highly nonlinear. This bias may lead to suboptimal study designs when GA is used to optimize the value of different studies. Therefore, we extend the conventional GA approach to improve its performance for nonlinear decision models. Our method provides accurate EVSI estimates by approximating the conditional expectation of the benefit based on 2 steps. First, a Taylor series approximation is applied to estimate the conditional expectation of the benefit as a function of the conditional moments of the parameters of interest using a spline, which is fitted to the samples of the parameters and the corresponding benefits. Next, the conditional moments of parameters are approximated by the conventional GA and Fisher information. The proposed approach is applied to several data collection exercises involving non-Gaussian parameters and nonlinear decision models. Its performance is compared with the nested Monte Carlo method, the conventional GA approach, and the nonparametric regression-based method for EVSI calculation. The proposed approach provides accurate EVSI estimates across different sample sizes when the parameters of interest are non-Gaussian and the decision models are nonlinear. The computational cost of the proposed method is similar to that of other novel methods. The proposed approach can estimate EVSI across sample sizes accurately and efficiently, which may support researchers in determining an economically optimal study design using EVSI. The Gaussian approximation method efficiently estimates the expected value of sample information (EVSI) for clinical trials with varying sample sizes, but it may introduce bias when health economic models have a nonlinear structure.We introduce the spline-based Taylor series approximation method and combine it with the original Gaussian approximation to correct the nonlinearity-induced bias in EVSI estimation.Our approach can provide more precise EVSI estimates for complex decision models without sacrificing computational efficiency, which can enhance the resource allocation strategies from the cost-effective perspective.

Adjusting for covariates representing potential confounders, mediators, or competing predictors in the presence of measurement error: Dispelling a potential misapprehension and insights for optimal study design with nutritional epidemiology examples

Background Variables such as dietary intake are measured with error yet frequently used in observational epidemiology. Although this limitation is sometimes noted, these variables are still often modeled as covariates without formal correction or sincere dialogue about measurement unreliability potentially weakening the validity of statistical conclusions. Further, larger sample sizes increase power (bias) to detect spurious correlations. Counterintuitively, recent work suggested a non-monotonic relationship between confounder unreliability and how much controlling for the confounder reduces (or induces) bias when testing for an exposure-outcome association. If true, such non-monotonicity would be especially concerning for applications such as nutrition, where measurement reliability varies substantially, and large sample sizes are common. Methods We offer a detailed derivations of the square partial correlation between the outcome and exposure, controlling for the confounder. In our derivation, the measurement reliabilities of exposures and confounders are not arbitrarily constrained to be equal. Further, our theoretical results are investigated using simulations. Results Reassuringly, these derivations and simulations show that the counterintuitive non-monotonicity relationship between confounder unreliability and how much controlling for the confounder reduces (or induces) bias when testing for an exposure-outcome association is an artifact of the arbitrary constraint which forces the measurement reliabilities of exposures and confounders to be equal, which that does not always hold. Conclusions The profound and manifold effects of measurement error on estimation and statistical conclusion validity in realistic scenarios indicate that merely mentioning measurement error as a limitation and then dispensing with it is not an adequate response. We also explore questions for optimal study design subject to resource constraints when considering reliability of exposures, covariates, and outcomes.